Journal of Pathology Informatics Journal of Pathology Informatics
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ORIGINAL ARTICLE
Year : 2017  |  Volume : 8  |  Issue : 1  |  Page : 34

Determining image processing features describing the appearance of challenging mitotic figures and miscounted nonmitotic objects


1 Medical Image Optimisation and Perception Research Group (MIOPeG), Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, Australia
2 Medical Image Optimisation and Perception Research Group (MIOPeG), Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, Australia; Department of Biomedical Informatics, University of Pittsburgh School of Medicine, USA

Correspondence Address:
Ziba Gandomkar
Brain and Mind Centre, 94 Mallett Street, Camperdown NSW 2050
Australia
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpi.jpi_22_17

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Context: Previous studies showed that the agreement among pathologists in recognition of mitoses in breast slides is fairly modest. Aims: Determining the significantly different quantitative features among easily identifiable mitoses, challenging mitoses, and miscounted nonmitoses within breast slides and identifying which color spaces capture the difference among groups better than others. Materials and Methods: The dataset contained 453 mitoses and 265 miscounted objects in breast slides. The mitoses were grouped into three categories based on the confidence degree of three pathologists who annotated them. The mitoses annotated as “probably a mitosis” by the majority of pathologists were considered as the challenging category. The miscounted objects were recognized as a mitosis or probably a mitosis by only one of the pathologists. The mitoses were segmented using k-means clustering, followed by morphological operations. Morphological, intensity-based, and textural features were extracted from the segmented area and also the image patch of 63 × 63 pixels in different channels of eight color spaces. Holistic features describing the mitoses' surrounding cells of each image were also extracted. Statistical Analysis Used: The Kruskal–Wallis H-test followed by the Tukey-Kramer test was used to identify significantly different features. Results: The results indicated that challenging mitoses were smaller and rounder compared to other mitoses. Among different features, the Gabor textural features differed more than others between challenging mitoses and the easily identifiable ones. Sizes of the non-mitoses were similar to easily identifiable mitoses, but nonmitoses were rounder. The intensity-based features from chromatin channels were the most discriminative features between the easily identifiable mitoses and the miscounted objects. Conclusions: Quantitative features can be used to describe the characteristics of challenging mitoses and miscounted nonmitotic objects.


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